Findings in cognitive neuroscience suggest that eye movements reflect cognitive processes [1]}, which are essential for understanding human intention during daily activities [2]}. Such an understanding of visual attention and intention can be valuable for many applications, including Augmented Reality (AR), Virtual Reality (VR), and Human-Robot Interaction (HRI). While wearable eye trackers are a standard way to obtain measurements of gaze behavior, they require calibration, consume significant power, and add substantial cost and complexity to wearable platforms. Alternatively, prior works [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]} seek to estimate the visual attention of the camera-wearer from videos captured from a first-person perspective. This task is known as egocentric gaze estimation.
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